Grid Intelligent Computing: How the AI brain that “doesn’t pile up computing power” can fill the gaps in forest scenes

From the consumer market to the industrial track, the "Red Sea" drone market has long been crowded with players from all walks of life, but there is still a niche area that even the leading manufacturers in the industry have rarely laid out - the forest scene. The industrial demand contained in the underforest scene is staggering: According to data from the National Forestry and Grassland Administration, my country's forest stock volume will reach 20.988 billion cubic meters in 2025, and national timber production will reach 140 million cubic meters; data from the Food and Agriculture Organization of the United Nations show that the annual global roundwood harvesting volume is approximately 4 billion cubic meters. Behind this is a large amount of work such as accounting for the stock volume in logged areas and measuring tree diameters at breast height. The understory environment is complex, and the traditional manual operation mode is costly, inefficient and dangerous. Although the industry has been calling for automation alternatives for a long time, the forest environment is a classic rejection environment, making it difficult for traditional automation solutions to be implemented. The understory is obscured by trees, making it easy for navigation equipment to be unable to locate due to loss of GNSS signals. Strong attenuation of communication signals will cause remote image transmission and manual control to be unable to be carried out in real time and coherently. Traditional SLAM technology cannot handle the complex texture of the environment that changes slightly at all times, such as tree branches swaying slightly with the wind. "Only by building an embodied intelligent solution that can run end-side and offline for real-time high-precision spatial perception and follow-up control can we effectively solve the operational needs of the 'total denial' environment under the forest," said Qian Min, head of the grid intelligent computing market. Established in 2025, Grid Intelligent Computing is committed to using its self-developed "grid domain learning technology" to form high-precision spatial understanding, and self-generated optimal control functions by the model, thereby achieving effective control capabilities such as complex obstacle avoidance. Based on this, the "GridAI Brain", an intelligent base adapted to a variety of hardware devices, has been launched, which for the first time enables drones to fly autonomously under the forest without GNSS, perform millimeter-level diameter measurement, and automatically avoid obstacles. The product has been commercially delivered in small batches. Dai Hao, chief scientist of Grid Intelligent Computing, has more than 30 years of technology accumulation. In 1996, he created the first algorithm system that combines heterogeneous architecture with high-performance computing. He was specially appointed as a researcher at the National High-Performance Computing Engineering Technology Research Center. Grid Intelligent Computing UAV 1. Using the grid domain learning mechanism to achieve spatial understanding capabilities of micro-data and small computing power The current mainstream UAV intelligent method